config_001.json

{

    "#": "---------------------------------------------------------------------",
    "#": " ARCHITECTURE ",
    "#": "---------------------------------------------------------------------",
        
    "#": "type: non-saturating (default), wasserstein",
    "#type": "non-saturating",
    "type": "wasserstein",
    "#type": "gradient_penalty",
    "gp_weight": 0.1,
    "layer_norm": false,
    
    "au_weight": 1,
    "lat_weight": 1,

    "#": "Number of layers for D and G",
    "d_layers": 3,
    "g_layers": 4,
    
    "#": "Number of neurons by layer for D and G",
    "d_dim": 400,
    "g_dim": 400,

    "#": "Number of dimension of the latent variable",
    "z_dim": 8,

    "#": "Clamp (only used in wasserstein mode)",
    "clamp_lower": -0.01,
    "clamp_upper": 0.01,

    
    "#": "---------------------------------------------------------------------",
    "#": " OPTIMISER ",
    "#": "---------------------------------------------------------------------",
        
    "#": "optimiser: adam RMSprop",
    "optimiser": "RMSprop",
    "#optimiser": "adam",

    "#": "optimiser: shuffle all batch ?",
    "shuffle": false,

    "#": "adam optimiser: regularisation L2 (for adam only) ; zero if no regul",
    "d_weight_decay": 0,
    "g_weight_decay": 0,

    "#": "adam optimiser: beta",
    "beta_1": "0.9",
    "beta_2": "0.99",

    "#": "optimiser: learning rate",
    "d_learning_rate": 1e-5,
    "g_learning_rate": 1e-5,

    "#": "optimiser: number of D and G update by epoch",
    "d_nb_update": 2,
    "g_nb_update": 1,

    "#": "optimiser: max nb of epoch (iteration)",
    "epoch": 550000,
    "plot_every_epoch": 400,
    "validation_every_epoch": 400,

    "#":"data for training",
    "validation_filename": "None",
    "start_pth":"None",
    "training_filename":"/net/scratch/people/plgztabor/primo_workdir/Generators/CGAN/Archiwum/hdf5/photons_without_anihilation.hd5",
    "model_name":"model.pth",
    "params_name":"params.pkl",
    "normalization_data_file":"/net/scratch/people/plgztabor/primo_workdir/Generators/CGAN/Archiwum/hdf5/normalizacja.dat",

    "start date":0.0,

    "#": "optimiser: nb of samples by batch",
    "batch_size": 10000,

    "#": "Smooth fake/real labels instead of zero/one",
    "#label_smoothing": 0.2,

    "leaky_relu":true,

    "#": "---------------------------------------------------------------------",
    "#": " DATA ",
    "#": "---------------------------------------------------------------------",
    "constraints": {
        "Ekine": [0,6],
        "X": [-200,200],
        "Y": [-200,200],
        "dX": [-1,1],
        "dY": [-1,1],
        "dZ": [0,1]
    },
    "keys": "Ekine X Y dX dY dZ E s a",
    "cond_keys": "E s a",
        
    "#": "---------------------------------------------------------------------",
    "#": " GENERAL ",
    "#": "---------------------------------------------------------------------",
        
    "#": "gpu_mode: true false auto",
    "gpu_mode": "auto",

    "#": "save Generator and info every epoch",
    "dump_epoch_start": 0,
    "dump_epoch_every": 5000,
    "dump_last_n_epoch": 0

}